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Codemesh

Intelligent code knowledge graph for AI coding agents

71% cheaper, 72% faster, 82% fewer tool calls vs baseline Grep+Read
on 6 real-world repos (Sonnet 4.6) — from a single codemesh index.

Benchmarks · Quick Start · Integrations · Write-Back · How It Works · API Reference · Full Results

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The Problem

AI coding agents waste 40-80% of their tokens on discovery — grepping through files, reading irrelevant code, and rebuilding context they've already seen in previous sessions.

On a 600-file codebase, a typical exploration task involves 10+ file reads before the agent even knows what's relevant.

Before:  Agent → Grep → 50 matches → Read 10 files → Understand → Work
After:   Agent → codemesh_explore → 3 relevant files → codemesh_trace → full path → Work

Codemesh is an MCP server that gives agents a persistent, queryable knowledge graph. The graph gets smarter over time: agents write back what they learn, so the next session starts informed.

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Benchmarks

Benchmarked on 6 real-world codebases (Alamofire, Excalidraw, VS Code, Swift Compiler, pydantic-validators, pydantic-basemodel) with Claude Sonnet 4.6, compared alongside baseline and graph-based approaches for context.

Full methodology, per-repo breakdowns, and pairwise comparisons: docs/benchmark-results.md | Early pydantic evals

Cost

Mode	Alamofire	Excalidraw	VS Code	Swift Compiler[^swift]	pydantic-validators	pydantic-basemodel	Avg
Baseline	$0.54	$0.89	$0.21	$0.83	$1.32	$0.78	$0.76
Codemesh MCP	$0.25	$0.21	$0.16	$0.23	$0.33	$0.13	$0.22
Codemesh CLI	$0.67	$0.51	$0.16	$0.83	$1.00	$0.18	$0.56
Codegraph	$0.37	$0.56	$0.57	$0.74	$0.29	$0.19	$0.45

Time

Mode	Alamofire	Excalidraw	VS Code	Swift[^swift]	pydantic-v	pydantic-b	Avg
Baseline	180s	191s	87s	199s	352s	232s	207s
Codemesh MCP	78s	45s	35s	87s	72s	32s	58s
Codemesh CLI	226s	177s	62s	227s	235s	51s	163s
Codegraph	134s	180s	192s	199s	75s	60s	140s

Tool calls (agent turns)

Mode	Alamofire	Excalidraw	VS Code	Swift[^swift]	pydantic-v	pydantic-b	Avg
Baseline	31	48	12	29	84	65	45
Codemesh MCP	9	5	3	14	14	3	8
Codemesh CLI	30	32	12	56	64	9	34
Codegraph	31	35	44	44	20	12	31

Quality (1–10, LLM-as-judge)

Mode	Alamofire[^alamo]	Excalidraw	VS Code	Swift Compiler	pydantic-validators	pydantic-basemodel	Avg
Baseline	n/a	9	8	7	2	9	7.0
Codemesh MCP	9	9	7	8	7	7.8	7.9
Codemesh CLI	9	7	7	9	1	8.4	6.9
Codegraph	8	9	8.7	8	8	9	8.4

Cost savings: Codemesh MCP vs Baseline

Repo	Baseline	Codemesh MCP	Cost saved	Time saved
Alamofire	$0.54	$0.25	−54%	−57% (180s → 78s)
Excalidraw	$0.89	$0.21	−76%	−76% (191s → 45s)
VS Code	$0.21	$0.16	−24%	−60% (87s → 35s)
Swift Compiler[^swift]	$0.83	$0.23	−72%	−56% (199s → 87s)
pydantic-validators	$1.32	$0.33	−75%	−79% (352s → 72s)
pydantic-basemodel	$0.78	$0.13	−83%	−86% (232s → 32s)
Average	$0.76	$0.22	−71%	−72%

[!NOTE] Codemesh MCP achieves the lowest cost and fastest time of any mode tested — 71% cheaper and 72% faster than baseline on average across 6 repos, using 82% fewer tool calls (8 vs 45). Quality is comparable to baseline (7.9 vs 7.0); Codegraph edges Codemesh on quality (8.4) but at roughly double the cost ($0.45 vs $0.22). Every repo shows cost and time savings — including the comprehension-heavy queries (Excalidraw, pydantic-basemodel) that regressed in prior builds of codemesh.

[^swift]: Swift Compiler's codemesh index failed to complete (indexer regression on 30k+ file codebases — see known issues). The codemesh numbers above reflect agent behavior with an empty retrieval graph, falling back to Read + LSP — still ahead of baseline, but unrepresentative of codemesh's capability on a properly-indexed Swift repo. [^alamo]: Baseline for Alamofire hit a judge error (score recorded as 0 but not meaningful); excluded from the Baseline average.

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Quick Start

1. Install

npm install -g @pyalwin/codemesh

Or run directly without installing:

npx -y @pyalwin/codemesh --help

<details> <summary>Build from source</summary>

git clone https://github.com/pyalwin/codemesh.git
cd codemesh
npm install && npm run build
npm link

</details>

Verify the install: codemesh --version should print the package version.

2. Index your project

cd /your/project
codemesh index --with-embeddings

Indexed 656 files
  Symbols found:  16733
  Edges created:  33266
  Duration:       10009ms
  PageRank:       13843 nodes scored
  Embeddings:     13187 symbols embedded

3. Choose your mode

Codemesh offers two ways to integrate with AI agents:

<details open> <summary><strong>Option A: MCP Server</strong> (structured tool calls)</summary>

Add to your Claude Code MCP config (~/.claude/mcp-servers.json or project .mcp.json):

{
  "mcpServers": {
    "codemesh": {
      "command": "npx",
      "args": ["-y", "@pyalwin/codemesh"],
      "env": {
        "CODEMESH_PROJECT_ROOT": "/path/to/your/project"
      }
    }
  }
}

The agent gets native MCP tools:

• codemesh_answer — one-call question answering (PRIMARY)
• codemesh_explore — search, context (multi-target), impact
• codemesh_trace — follow call chains
• codemesh_enrich / codemesh_workflow — write back
• codemesh_status — health check

Best for: Opus, structured workflows, enrichment/write-back

</details>

<details> <summary><strong>Option B: CLI Mode</strong> (via Bash — zero MCP overhead)</summary>

No MCP config needed. The agent calls codemesh directly via Bash:

export CODEMESH_PROJECT_ROOT=/path/to/your/project

# Primary — one-call question answering:
codemesh explore answer "How does request handling work?"

# Follow-up commands:
codemesh explore search "request flow"
codemesh explore context Source/Core/Session.swift Source/Core/Request.swift
codemesh explore trace Session.request --depth 5
codemesh explore semantic "network request handling"  # requires --with-embeddings

All commands return JSON to stdout. No MCP server process, no protocol overhead.

Best for: Sonnet/Haiku, speed-sensitive workflows, simpler setup

</details>

Which mode should I use?

	MCP Server	CLI Mode
Setup	MCP config file	Just export CODEMESH_PROJECT_ROOT
Overhead	MCP protocol per call	Zero — direct subprocess
Enrichment	Native codemesh_enrich tool	Via Bash("codemesh enrich ...")
Best model	Opus (follows MCP well)	Sonnet (55% cheaper, 61% faster than baseline)
Recommended	Complex codebases	Default choice

4. Use it

The agent now has 6 new tools. Query the graph before reading code:

You: "Find how pydantic handles validation"

Agent calls: codemesh_answer({ question: "How does pydantic handle validation?" })
       gets: 9 relevant files ranked by PageRank, call chains, 
             git hotspots, co-change relationships, 5 suggested reads

Agent calls: Read("pydantic/functional_validators.py", lines 1-50)
       reads: only the specific lines suggested by the answer tool

Agent calls: codemesh_enrich({ path: "pydantic/functional_validators.py",
               summary: "Primary V2 validator API..." })
       saves: summary for next session

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Client Integrations

Codemesh speaks the Model Context Protocol, so any MCP-compatible client can use it. Paste one of the snippets below, restart the client, and the six codemesh_* tools show up in the agent's toolbox.

<details open> <summary><strong>Claude Code</strong> (CLI)</summary>

Add to ~/.claude/mcp-servers.json (user-wide) or .mcp.json (project-local):

{
  "mcpServers": {
    "codemesh": {
      "command": "npx",
      "args": ["-y", "@pyalwin/codemesh"],
      "env": {
        "CODEMESH_PROJECT_ROOT": "/absolute/path/to/your/project"
      }
    }
  }
}

</details>

<details> <summary><strong>Claude Desktop</strong> (macOS / Windows app)</summary>

Edit claude_desktop_config.json:

• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
• Windows: %APPDATA%\Claude\claude_desktop_config.json

{
  "mcpServers": {
    "codemesh": {
      "command": "npx",
      "args": ["-y", "@pyalwin/codemesh"],
      "env": {
        "CODEMESH_PROJECT_ROOT": "/absolute/path/to/your/project"
      }
    }
  }
}

Restart Claude Desktop. Codemesh's tools will appear in the tool picker (hammer icon). </details>

<details> <summary><strong>Cursor</strong> — stop the agent from wandering your codebase</summary>

Cursor reads .cursor/mcp.json per project (or ~/.cursor/mcp.json for all projects):

{
  "mcpServers": {
    "codemesh": {
      "command": "npx",
      "args": ["-y", "@pyalwin/codemesh"],
      "env": {
        "CODEMESH_PROJECT_ROOT": "${workspaceFolder}"
      }
    }
  }
}

Open Settings → MCP, confirm codemesh is green, then mention it in a prompt (@codemesh how does auth work?) to nudge the agent toward graph queries instead of recursive Grep. </details>

<details> <summary><strong>Windsurf / VS Code (Continue)</strong></summary>

Add to ~/.continue/config.json under experimental.modelContextProtocolServers:

{
  "experimental": {
    "modelContextProtocolServers": [
      {
        "transport": {
          "type": "stdio",
          "command": "npx",
          "args": ["-y", "@pyalwin/codemesh"],
          "env": {
            "CODEMESH_PROJECT_ROOT": "/absolute/path/to/your/project"
          }
        }
      }
    ]
  }
}

</details>

---

Agent Write-Back: the graph that gets smarter

Every other code-intelligence tool indexes your repo once and hands the agent a read-only view. Codemesh lets the agent teach the graph as it works — summaries, workflows, and cross-concept links persist across sessions and survive re-indexing.

// Session 1 — agent reads unfamiliar code, then writes back what it learned.
codemesh_enrich({
  path: "pydantic/functional_validators.py",
  summary: "Primary V2 validator API. `@field_validator` wraps "
         + "`_decorators.FieldValidatorDecoratorInfo`; `mode='before'|'after'` "
         + "toggles pre/post-coercion execution. Extends BaseValidator.",
  concepts: ["validation", "decorators", "v2-api"]
})

// Session 1 — agent traces a multi-file flow, records the path.
codemesh_workflow({
  name: "pydantic field validation",
  description: "Request → BaseModel.__init__ → SchemaValidator → field_validator",
  files: [
    "pydantic/main.py",
    "pydantic/_internal/_model_construction.py",
    "pydantic/functional_validators.py"
  ]
})

// Session 2 (days later) — same question, different agent instance.
codemesh_answer({ question: "How does pydantic validate fields?" })
// → returns the enriched summary AND the 3-file workflow from Session 1
//   before the agent reads a single line. Zero rediscovery cost.

The graph now knows things no static analyzer could infer: why a file matters, which files move together, what a maintainer called a concept. Re-indexing rebuilds the structural layer (files, symbols, imports, calls) but preserves every enrichment — entries only go stale when their referenced files change.

See codemesh_enrich and codemesh_workflow under MCP Tools.

---

How It Works

                      ┌──────────────────────────────────┐
                      │         Knowledge Graph           │
                      │                                   │
                      │  ┌──────────┐ ┌───────────────┐  │
                      │  │Structural│ │   Semantic     │  │
                      │  │  (auto)  │ │   (agents)    │  │
                      │  │          │ │               │  │
                      │  │ files    │ │ summaries     │  │
                      │  │ symbols  │ │ workflows     │  │
                      │  │ imports  │ │ concepts      │  │
                      │  │ calls    │ │ enrichments   │  │
                      │  └──────────┘ └───────────────┘  │
                      │                                   │
                      │  ┌──────────┐ ┌───────────────┐  │
                      │  │   Git    │ │   Search      │  │
                      │  │  Intel   │ │               │  │
                      │  │          │ │ FTS5 (exact)  │  │
                      │  │ hotspots │ │ Trigram (fuzzy)│  │
                      │  │ co-change│ │ LanceDB (sem) │  │
                      │  │ churn    │ │ PageRank      │  │
                      │  └──────────┘ └───────────────┘  │
                      │                                   │
                      │        SQLite + LanceDB           │
                      └────────────┬──────────────────────┘
                                   │
                      ┌────────────┴──────────────────────┐
                      │    MCP Server / CLI (7 tools)      │
                      │                                    │
                      │  answer · explore · trace          │
                      │  enrich · workflow · status         │
                      └────────────────────────────────────┘

Structural layer (automatic) — Tree-sitter parses your code into files, symbols (functions, classes, methods), and relationships (imports, calls, extends). Rebuilt on each index.

Semantic layer (agent-built) — As agents work with your code, they write back summaries and workflow paths. These survive re-indexing and accumulate across sessions. Invalidated when referenced files change.

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MCP Tools

Tool	Purpose	Example
codemesh_answer	One-call context assembly — returns all relevant files, call chains, hotspots, suggested reads	codemesh_answer({ question: "How does auth work?" })
codemesh_explore	Search, context (multi-target), impact analysis	codemesh_explore({ action: "search", query: "auth" })
codemesh_trace	Follow call chains with source code	codemesh_trace({ symbol: "login", depth: 5 })
codemesh_enrich	Write back what you learned for future sessions	codemesh_enrich({ path: "src/auth.py", summary: "..." })
codemesh_workflow	Record multi-file workflow paths	codemesh_workflow({ name: "login flow", files: [...] })
codemesh_status	Graph health check	codemesh_status()

---

CLI

codemesh index                          # structural + git intel + pagerank
codemesh index --with-embeddings        # + semantic vectors (~80MB model, zero API cost)
codemesh status                         # graph statistics
codemesh rebuild                        # purge and re-index

codemesh explore answer "question"      # one-call context assembly (PRIMARY)
codemesh explore search "query"         # FTS5 + trigram + semantic search
codemesh explore context file1 file2    # multi-target context
codemesh explore trace symbol --depth 5 # follow call chains
codemesh explore semantic "query"       # vector similarity (needs embeddings)
codemesh explore impact file            # reverse dependencies

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Optional: Hooks & Skills

<details> <summary><strong>Skill</strong> — teaches agents the graph-first workflow</summary>

Copy skills/codemesh.md to ~/.claude/skills/ or your project's .claude/skills/.

# Install the skill so Claude Code loads the workflow automatically
cp /path/to/codemesh/skills/codemesh.md /your/project/.claude/skills/

The skill instructs agents to query the graph before using Grep/Read, and to write back via codemesh_enrich after reading code. </details>

<details> <summary><strong>Hooks</strong> — automatic pre-read context injection</summary>

Add to .claude/settings.json:

{
  "hooks": {
    "pre_tool_use": [{
      "matcher": "Read",
      "command": "/path/to/codemesh/hooks/pre-read.sh"
    }],
    "post_tool_use": [{
      "matcher": "Read",
      "command": "/path/to/codemesh/hooks/post-read.sh"
    }]
  }
}

• Pre-read — Injects cached summaries before file reads
• Post-read — Nudges the agent to enrich after reading unfamiliar files </details>

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Supported Languages

<table> <tr> <td>TypeScript</td><td>JavaScript</td><td>Python</td><td>Go</td><td>Rust</td><td>Java</td><td>C#</td> </tr> <tr> <td>Ruby</td><td>PHP</td><td>C</td><td>C++</td><td>Swift</td><td>Kotlin</td><td>Dart</td> </tr> </table>

Any language with a tree-sitter grammar can be added.

---

Graph Data Model

Nodes

Type	Source	Key Fields
file	Static (tree-sitter)	path, hash, last_indexed_at
symbol	Static (tree-sitter)	name, kind, file_path, line_start, line_end, signature
concept	Agent-written	summary, last_updated_by, stale
workflow	Agent-written	description, file_sequence, last_walked_at

Edges

Type	Direction	Source
contains	file → symbol	Static
imports	file → file	Static
calls	symbol → symbol	Static
extends	symbol → symbol	Static
describes	concept → file/symbol	Agent
related_to	concept → concept	Agent
traverses	workflow → file	Agent

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Architecture

codemesh/
├── src/
│   ├── index.ts              # MCP server entry (stdio transport)
│   ├── server.ts             # Tool registration (zod schemas)
│   ├── graph/
│   │   ├── types.ts          # Node/edge type definitions
│   │   ├── storage.ts        # StorageBackend interface (swappable)
│   │   └── sqlite.ts         # SQLite + FTS5 implementation
│   ├── indexer/
│   │   ├── indexer.ts        # File walking, hashing, incremental indexing
│   │   ├── parser.ts         # Tree-sitter AST extraction
│   │   └── languages.ts      # Language registry (ext → grammar)
│   ├── tools/                # 6 MCP tool handlers
│   └── cli.ts                # CLI entry point
├── skills/codemesh.md        # Agent education skill
├── hooks/                    # Pre/post read hooks
└── eval/                     # Eval framework (5 tasks, 3 models)

Storage is backend-agnostic. The StorageBackend interface abstracts all persistence. v1 uses SQLite with FTS5 for zero-dependency local operation. The interface supports swapping to Memgraph, Neo4j, or other graph databases.

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Eval Framework

Reproducible evaluation harness with LLM-as-judge scoring:

# Setup
npm install -g @pyalwin/codemesh
git clone --depth 1 https://github.com/Alamofire/Alamofire.git /tmp/alamofire
# ... clone other repos ...

# Index
CODEMESH_PROJECT_ROOT=/tmp/alamofire codemesh index

# Run benchmarks
python3 eval/head_to_head.py --model sonnet alamofire excalidraw vscode swift-compiler

See docs/benchmark-results.md for full methodology and results. Early pydantic evals are archived in docs/experiments/.

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vs. Existing Tools

Feature	CodeGraph	Graphify	Axon	Codemesh
Structural indexing	Yes	Yes	Yes	Yes
FTS search	Yes	—	Yes	Yes
Agent write-back	—	—	—	Yes
Workflow memory	—	—	—	Yes
Hook interception	—	—	—	Yes
Backend-swappable	—	—	—	Yes
Eval framework	—	—	—	Yes
Published benchmarks	—	—	—	Yes

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Development

bun install          # Install dependencies
bun run build        # Compile TypeScript
bun run test         # Run 102 tests
bun run dev          # Watch mode
bun run lint         # Type check

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Contributing

Contributions welcome. Areas for improvement:

• More languages — Add tree-sitter grammars and language-specific extractors
• AST-diff invalidation — Function-level instead of file-level staleness detection
• Graph backends — Memgraph/Neo4j adapters for StorageBackend
• Semantic search — Embedding columns alongside FTS5
• Agent adoption — Better patterns for agents to prefer graph tools naturally

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License

MIT